Overview

Dataset statistics

Number of variables23
Number of observations3803
Missing cells6996
Missing cells (%)8.0%
Duplicate rows126
Duplicate rows (%)3.3%
Total size in memory683.5 KiB
Average record size in memory184.0 B

Variable types

Categorical10
Text3
Numeric10

Alerts

Dataset has 126 (3.3%) duplicate rowsDuplicates
price is highly overall correlated with property_type and 3 other fieldsHigh correlation
area is highly overall correlated with built_up_area and 1 other fieldsHigh correlation
bedRoom is highly overall correlated with property_type and 5 other fieldsHigh correlation
bathroom is highly overall correlated with property_type and 5 other fieldsHigh correlation
super_built_up_area is highly overall correlated with price and 3 other fieldsHigh correlation
built_up_area is highly overall correlated with areaHigh correlation
carpet_area is highly overall correlated with areaHigh correlation
servant room is highly overall correlated with bedRoom and 2 other fieldsHigh correlation
price_per_sqft is highly overall correlated with priceHigh correlation
property_type is highly overall correlated with price and 3 other fieldsHigh correlation
balcony is highly overall correlated with bedRoom and 1 other fieldsHigh correlation
floorNum is highly overall correlated with property_typeHigh correlation
store room is highly imbalanced (56.2%)Imbalance
facing has 1105 (29.1%) missing valuesMissing
super_built_up_area has 1888 (49.6%) missing valuesMissing
built_up_area has 2070 (54.4%) missing valuesMissing
carpet_area has 1859 (48.9%) missing valuesMissing
area is highly skewed (γ1 = 30.23273447)Skewed
built_up_area is highly skewed (γ1 = 41.21758008)Skewed
carpet_area is highly skewed (γ1 = 24.7960836)Skewed
floorNum has 134 (3.5%) zerosZeros
luxury_score has 486 (12.8%) zerosZeros

Reproduction

Analysis started2025-11-26 14:16:20.062429
Analysis finished2025-11-26 14:17:08.413793
Duration48.35 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

property_type
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.8 KiB
flat
2943 
house
860 

Length

Max length5
Median length4
Mean length4.2261373
Min length4

Characters and Unicode

Total characters16072
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowflat
2nd rowflat
3rd rowhouse
4th rowflat
5th rowhouse

Common Values

ValueCountFrequency (%)
flat2943
77.4%
house860
 
22.6%

Length

2025-11-26T19:47:09.378189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T19:47:09.673509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
flat2943
77.4%
house860
 
22.6%

Most occurring characters

ValueCountFrequency (%)
f2943
18.3%
l2943
18.3%
a2943
18.3%
t2943
18.3%
h860
 
5.4%
o860
 
5.4%
u860
 
5.4%
s860
 
5.4%
e860
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)16072
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f2943
18.3%
l2943
18.3%
a2943
18.3%
t2943
18.3%
h860
 
5.4%
o860
 
5.4%
u860
 
5.4%
s860
 
5.4%
e860
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)16072
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f2943
18.3%
l2943
18.3%
a2943
18.3%
t2943
18.3%
h860
 
5.4%
o860
 
5.4%
u860
 
5.4%
s860
 
5.4%
e860
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)16072
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f2943
18.3%
l2943
18.3%
a2943
18.3%
t2943
18.3%
h860
 
5.4%
o860
 
5.4%
u860
 
5.4%
s860
 
5.4%
e860
 
5.4%

society
Text

Distinct676
Distinct (%)17.8%
Missing1
Missing (%)< 0.1%
Memory size29.8 KiB
2025-11-26T19:47:10.445999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length49
Median length39
Mean length16.922672
Min length1

Characters and Unicode

Total characters64340
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique291 ?
Unique (%)7.7%

Sample

1st rowdlf the skycourt
2nd rowss the leaf
3rd rowansals florence villa
4th rowrof ananda
5th rowemaar mgf marbella
ValueCountFrequency (%)
independent491
 
4.9%
the362
 
3.6%
dlf225
 
2.2%
park219
 
2.2%
city172
 
1.7%
global165
 
1.6%
signature161
 
1.6%
emaar159
 
1.6%
m3m156
 
1.6%
heights139
 
1.4%
Other values (783)7779
77.6%
2025-11-26T19:47:11.903205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e6935
 
10.8%
6228
 
9.7%
a6090
 
9.5%
r4355
 
6.8%
n4270
 
6.6%
i3970
 
6.2%
t3851
 
6.0%
s3627
 
5.6%
l3074
 
4.8%
o2867
 
4.5%
Other values (31)19073
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)64340
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e6935
 
10.8%
6228
 
9.7%
a6090
 
9.5%
r4355
 
6.8%
n4270
 
6.6%
i3970
 
6.2%
t3851
 
6.0%
s3627
 
5.6%
l3074
 
4.8%
o2867
 
4.5%
Other values (31)19073
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)64340
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e6935
 
10.8%
6228
 
9.7%
a6090
 
9.5%
r4355
 
6.8%
n4270
 
6.6%
i3970
 
6.2%
t3851
 
6.0%
s3627
 
5.6%
l3074
 
4.8%
o2867
 
4.5%
Other values (31)19073
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)64340
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e6935
 
10.8%
6228
 
9.7%
a6090
 
9.5%
r4355
 
6.8%
n4270
 
6.6%
i3970
 
6.2%
t3851
 
6.0%
s3627
 
5.6%
l3074
 
4.8%
o2867
 
4.5%
Other values (31)19073
29.6%

sector
Text

Distinct131
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size29.8 KiB
2025-11-26T19:47:12.583407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length26
Median length9
Mean length9.6463318
Min length7

Characters and Unicode

Total characters36685
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowsector 86
2nd rowsector 85
3rd rowsector 57
4th rowsector 95
5th rowsector 66
ValueCountFrequency (%)
sector3536
45.2%
road245
 
3.1%
sohna233
 
3.0%
102113
 
1.4%
85110
 
1.4%
92105
 
1.3%
6994
 
1.2%
9091
 
1.2%
6590
 
1.1%
8190
 
1.1%
Other values (121)3120
39.9%
2025-11-26T19:47:13.583049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o4098
11.2%
4024
11.0%
s3921
10.7%
r3905
10.6%
e3715
10.1%
c3662
10.0%
t3612
9.8%
11120
 
3.1%
a916
 
2.5%
0825
 
2.2%
Other values (24)6887
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)36685
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o4098
11.2%
4024
11.0%
s3921
10.7%
r3905
10.6%
e3715
10.1%
c3662
10.0%
t3612
9.8%
11120
 
3.1%
a916
 
2.5%
0825
 
2.2%
Other values (24)6887
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)36685
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o4098
11.2%
4024
11.0%
s3921
10.7%
r3905
10.6%
e3715
10.1%
c3662
10.0%
t3612
9.8%
11120
 
3.1%
a916
 
2.5%
0825
 
2.2%
Other values (24)6887
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)36685
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o4098
11.2%
4024
11.0%
s3921
10.7%
r3905
10.6%
e3715
10.1%
c3662
10.0%
t3612
9.8%
11120
 
3.1%
a916
 
2.5%
0825
 
2.2%
Other values (24)6887
18.8%

price
Real number (ℝ)

High correlation 

Distinct473
Distinct (%)12.5%
Missing18
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2.5058045
Minimum0.07
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2025-11-26T19:47:13.863010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.37
Q10.94
median1.5
Q32.7
95-th percentile8.49
Maximum31.5
Range31.43
Interquartile range (IQR)1.76

Descriptive statistics

Standard deviation2.9501212
Coefficient of variation (CV)1.177315
Kurtosis15.257819
Mean2.5058045
Median Absolute Deviation (MAD)0.71
Skewness3.3113347
Sum9484.47
Variance8.703215
MonotonicityNot monotonic
2025-11-26T19:47:14.202763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2583
 
2.2%
0.968
 
1.8%
1.566
 
1.7%
1.266
 
1.7%
1.166
 
1.7%
1.463
 
1.7%
1.360
 
1.6%
0.9558
 
1.5%
256
 
1.5%
151
 
1.3%
Other values (463)3148
82.8%
ValueCountFrequency (%)
0.071
 
< 0.1%
0.161
 
< 0.1%
0.171
 
< 0.1%
0.191
 
< 0.1%
0.29
0.2%
0.216
0.2%
0.229
0.2%
0.231
 
< 0.1%
0.247
0.2%
0.2511
0.3%
ValueCountFrequency (%)
31.51
 
< 0.1%
27.51
 
< 0.1%
262
0.1%
251
 
< 0.1%
241
 
< 0.1%
231
 
< 0.1%
221
 
< 0.1%
203
0.1%
19.52
0.1%
193
0.1%

price_per_sqft
Real number (ℝ)

High correlation 

Distinct2651
Distinct (%)70.0%
Missing18
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean13800.168
Minimum4
Maximum600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2025-11-26T19:47:14.556004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4718.2
Q16808
median9000
Q313765
95-th percentile33308.2
Maximum600000
Range599996
Interquartile range (IQR)6957

Descriptive statistics

Standard deviation23052.006
Coefficient of variation (CV)1.6704149
Kurtosis187.04187
Mean13800.168
Median Absolute Deviation (MAD)2758
Skewness11.43922
Sum52233635
Variance5.3139496 × 108
MonotonicityNot monotonic
2025-11-26T19:47:14.983373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000028
 
0.7%
800019
 
0.5%
500017
 
0.4%
1250017
 
0.4%
1111114
 
0.4%
750014
 
0.4%
666614
 
0.4%
2222213
 
0.3%
833313
 
0.3%
3333311
 
0.3%
Other values (2641)3625
95.3%
(Missing)18
 
0.5%
ValueCountFrequency (%)
41
< 0.1%
51
< 0.1%
71
< 0.1%
91
< 0.1%
531
< 0.1%
571
< 0.1%
582
0.1%
601
< 0.1%
611
< 0.1%
791
< 0.1%
ValueCountFrequency (%)
6000001
< 0.1%
4000001
< 0.1%
3157891
< 0.1%
3083331
< 0.1%
2909481
< 0.1%
2833331
< 0.1%
2666661
< 0.1%
2611941
< 0.1%
2453981
< 0.1%
2416661
< 0.1%

area
Real number (ℝ)

High correlation  Skewed 

Distinct1312
Distinct (%)34.7%
Missing18
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2845.9995
Minimum50
Maximum875000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2025-11-26T19:47:15.348825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile519
Q11220
median1725
Q32295
95-th percentile4200
Maximum875000
Range874950
Interquartile range (IQR)1075

Descriptive statistics

Standard deviation22783.349
Coefficient of variation (CV)8.0053947
Kurtosis974.19183
Mean2845.9995
Median Absolute Deviation (MAD)525
Skewness30.232734
Sum10772108
Variance5.1908099 × 108
MonotonicityNot monotonic
2025-11-26T19:47:15.744270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165055
 
1.4%
135051
 
1.3%
180048
 
1.3%
195044
 
1.2%
324043
 
1.1%
270039
 
1.0%
90039
 
1.0%
200035
 
0.9%
240025
 
0.7%
225025
 
0.7%
Other values (1302)3381
88.9%
ValueCountFrequency (%)
504
0.1%
551
 
< 0.1%
561
 
< 0.1%
571
 
< 0.1%
602
0.1%
611
 
< 0.1%
672
0.1%
701
 
< 0.1%
721
 
< 0.1%
761
 
< 0.1%
ValueCountFrequency (%)
8750001
< 0.1%
6428571
< 0.1%
6200001
< 0.1%
5666671
< 0.1%
2155171
< 0.1%
989781
< 0.1%
827811
< 0.1%
655172
0.1%
652611
< 0.1%
582281
< 0.1%
Distinct2355
Distinct (%)61.9%
Missing0
Missing (%)0.0%
Memory size29.8 KiB
2025-11-26T19:47:16.730320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length124
Median length119
Mean length53.841967
Min length12

Characters and Unicode

Total characters204761
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1780 ?
Unique (%)46.8%

Sample

1st rowSuper Built up area 1929(179.21 sq.m.)Built Up area: 1750 sq.ft. (162.58 sq.m.)Carpet area: 1450 sq.ft. (134.71 sq.m.)
2nd rowSuper Built up area 1640(152.36 sq.m.)
3rd rowPlot area 300(250.84 sq.m.)
4th rowCarpet area: 34401 (3195.96 sq.m.)
5th rowPlot area 500(418.06 sq.m.)
ValueCountFrequency (%)
area5728
18.5%
sq.m3779
12.2%
up3102
 
10.0%
built2393
 
7.7%
super1915
 
6.2%
sq.ft1779
 
5.7%
sq.m.)carpet1208
 
3.9%
carpet732
 
2.4%
sq.m.)built707
 
2.3%
plot682
 
2.2%
Other values (2846)8965
28.9%
2025-11-26T19:47:18.105698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
27187
 
13.3%
.20907
 
10.2%
a13536
 
6.6%
r9723
 
4.7%
e9587
 
4.7%
19460
 
4.6%
s7747
 
3.8%
q7611
 
3.7%
t7507
 
3.7%
p6961
 
3.4%
Other values (25)84535
41.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)204761
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
27187
 
13.3%
.20907
 
10.2%
a13536
 
6.6%
r9723
 
4.7%
e9587
 
4.7%
19460
 
4.6%
s7747
 
3.8%
q7611
 
3.7%
t7507
 
3.7%
p6961
 
3.4%
Other values (25)84535
41.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)204761
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
27187
 
13.3%
.20907
 
10.2%
a13536
 
6.6%
r9723
 
4.7%
e9587
 
4.7%
19460
 
4.6%
s7747
 
3.8%
q7611
 
3.7%
t7507
 
3.7%
p6961
 
3.4%
Other values (25)84535
41.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)204761
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
27187
 
13.3%
.20907
 
10.2%
a13536
 
6.6%
r9723
 
4.7%
e9587
 
4.7%
19460
 
4.6%
s7747
 
3.8%
q7611
 
3.7%
t7507
 
3.7%
p6961
 
3.4%
Other values (25)84535
41.3%

bedRoom
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3381541
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2025-11-26T19:47:18.395936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8767336
Coefficient of variation (CV)0.56220699
Kurtosis18.610254
Mean3.3381541
Median Absolute Deviation (MAD)1
Skewness3.511539
Sum12695
Variance3.5221288
MonotonicityNot monotonic
2025-11-26T19:47:18.763437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
31545
40.6%
2993
26.1%
4676
17.8%
5213
 
5.6%
1130
 
3.4%
675
 
2.0%
941
 
1.1%
830
 
0.8%
1228
 
0.7%
728
 
0.7%
Other values (9)44
 
1.2%
ValueCountFrequency (%)
1130
 
3.4%
2993
26.1%
31545
40.6%
4676
17.8%
5213
 
5.6%
675
 
2.0%
728
 
0.7%
830
 
0.8%
941
 
1.1%
1020
 
0.5%
ValueCountFrequency (%)
211
 
< 0.1%
201
 
< 0.1%
192
 
0.1%
182
 
0.1%
1612
0.3%
141
 
< 0.1%
134
 
0.1%
1228
0.7%
111
 
< 0.1%
1020
0.5%

bathroom
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4054694
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2025-11-26T19:47:19.046878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9304562
Coefficient of variation (CV)0.56686936
Kurtosis17.745175
Mean3.4054694
Median Absolute Deviation (MAD)1
Skewness3.2570832
Sum12951
Variance3.7266613
MonotonicityNot monotonic
2025-11-26T19:47:19.314783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
31112
29.2%
21105
29.1%
4839
22.1%
5299
 
7.9%
1160
 
4.2%
6120
 
3.2%
741
 
1.1%
941
 
1.1%
826
 
0.7%
1222
 
0.6%
Other values (9)38
 
1.0%
ValueCountFrequency (%)
1160
 
4.2%
21105
29.1%
31112
29.2%
4839
22.1%
5299
 
7.9%
6120
 
3.2%
741
 
1.1%
826
 
0.7%
941
 
1.1%
109
 
0.2%
ValueCountFrequency (%)
211
 
< 0.1%
203
 
0.1%
184
 
0.1%
173
 
0.1%
168
 
0.2%
142
 
0.1%
134
 
0.1%
1222
0.6%
114
 
0.1%
109
0.2%

balcony
Categorical

High correlation 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.8 KiB
3+
1202 
3
1110 
2
925 
1
376 
0
190 

Length

Max length2
Median length1
Mean length1.3160663
Min length1

Characters and Unicode

Total characters5005
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row2
4th row1
5th row3+

Common Values

ValueCountFrequency (%)
3+1202
31.6%
31110
29.2%
2925
24.3%
1376
 
9.9%
0190
 
5.0%

Length

2025-11-26T19:47:19.628310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T19:47:19.918643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
32312
60.8%
2925
24.3%
1376
 
9.9%
0190
 
5.0%

Most occurring characters

ValueCountFrequency (%)
32312
46.2%
+1202
24.0%
2925
18.5%
1376
 
7.5%
0190
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)5005
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
32312
46.2%
+1202
24.0%
2925
18.5%
1376
 
7.5%
0190
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5005
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
32312
46.2%
+1202
24.0%
2925
18.5%
1376
 
7.5%
0190
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5005
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
32312
46.2%
+1202
24.0%
2925
18.5%
1376
 
7.5%
0190
 
3.8%

floorNum
Real number (ℝ)

High correlation  Zeros 

Distinct43
Distinct (%)1.1%
Missing19
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean6.8102537
Minimum0
Maximum51
Zeros134
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2025-11-26T19:47:20.267738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q310
95-th percentile18
Maximum51
Range51
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.0275551
Coefficient of variation (CV)0.88507056
Kurtosis4.5493229
Mean6.8102537
Median Absolute Deviation (MAD)3
Skewness1.6987333
Sum25770
Variance36.33142
MonotonicityNot monotonic
2025-11-26T19:47:20.601579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3513
13.5%
2506
13.3%
1365
 
9.6%
4328
 
8.6%
8197
 
5.2%
6187
 
4.9%
10186
 
4.9%
7183
 
4.8%
5177
 
4.7%
9170
 
4.5%
Other values (33)972
25.6%
ValueCountFrequency (%)
0134
 
3.5%
1365
9.6%
2506
13.3%
3513
13.5%
4328
8.6%
5177
 
4.7%
6187
 
4.9%
7183
 
4.8%
8197
 
5.2%
9170
 
4.5%
ValueCountFrequency (%)
511
 
< 0.1%
451
 
< 0.1%
441
 
< 0.1%
432
0.1%
402
0.1%
392
0.1%
381
 
< 0.1%
352
0.1%
342
0.1%
334
0.1%

facing
Categorical

Missing 

Distinct8
Distinct (%)0.3%
Missing1105
Missing (%)29.1%
Memory size29.8 KiB
East
642 
North-East
639 
North
398 
West
255 
South
233 
Other values (3)
531 

Length

Max length10
Median length5
Mean length6.8358043
Min length4

Characters and Unicode

Total characters18443
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth
2nd rowSouth
3rd rowNorth
4th rowEast
5th rowNorth-West

Common Values

ValueCountFrequency (%)
East642
16.9%
North-East639
16.8%
North398
 
10.5%
West255
 
6.7%
South233
 
6.1%
North-West200
 
5.3%
South-East174
 
4.6%
South-West157
 
4.1%
(Missing)1105
29.1%

Length

2025-11-26T19:47:20.952121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T19:47:21.334471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
east642
23.8%
north-east639
23.7%
north398
14.8%
west255
 
9.5%
south233
 
8.6%
north-west200
 
7.4%
south-east174
 
6.4%
south-west157
 
5.8%

Most occurring characters

ValueCountFrequency (%)
t3868
21.0%
s2067
11.2%
o1801
9.8%
h1801
9.8%
E1455
 
7.9%
a1455
 
7.9%
N1237
 
6.7%
r1237
 
6.7%
-1170
 
6.3%
W612
 
3.3%
Other values (3)1740
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)18443
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t3868
21.0%
s2067
11.2%
o1801
9.8%
h1801
9.8%
E1455
 
7.9%
a1455
 
7.9%
N1237
 
6.7%
r1237
 
6.7%
-1170
 
6.3%
W612
 
3.3%
Other values (3)1740
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)18443
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t3868
21.0%
s2067
11.2%
o1801
9.8%
h1801
9.8%
E1455
 
7.9%
a1455
 
7.9%
N1237
 
6.7%
r1237
 
6.7%
-1170
 
6.3%
W612
 
3.3%
Other values (3)1740
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)18443
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t3868
21.0%
s2067
11.2%
o1801
9.8%
h1801
9.8%
E1455
 
7.9%
a1455
 
7.9%
N1237
 
6.7%
r1237
 
6.7%
-1170
 
6.3%
W612
 
3.3%
Other values (3)1740
9.4%

agePossession
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size29.8 KiB
Relatively New
1676 
New Property
626 
Moderately Old
575 
Undefined
484 
Old Property
310 

Length

Max length18
Median length14
Mean length13.010255
Min length9

Characters and Unicode

Total characters49478
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRelatively New
2nd rowRelatively New
3rd rowOld Property
4th rowRelatively New
5th rowModerately Old

Common Values

ValueCountFrequency (%)
Relatively New1676
44.1%
New Property626
 
16.5%
Moderately Old575
 
15.1%
Undefined484
 
12.7%
Old Property310
 
8.2%
Under Construction132
 
3.5%

Length

2025-11-26T19:47:21.709701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T19:47:21.991333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
new2302
32.3%
relatively1676
23.5%
property936
13.1%
old885
 
12.4%
moderately575
 
8.1%
undefined484
 
6.8%
under132
 
1.9%
construction132
 
1.9%

Most occurring characters

ValueCountFrequency (%)
e8840
17.9%
l4812
 
9.7%
t3451
 
7.0%
3319
 
6.7%
y3187
 
6.4%
r2711
 
5.5%
d2560
 
5.2%
N2302
 
4.7%
w2302
 
4.7%
i2292
 
4.6%
Other values (15)13702
27.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)49478
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e8840
17.9%
l4812
 
9.7%
t3451
 
7.0%
3319
 
6.7%
y3187
 
6.4%
r2711
 
5.5%
d2560
 
5.2%
N2302
 
4.7%
w2302
 
4.7%
i2292
 
4.6%
Other values (15)13702
27.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)49478
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e8840
17.9%
l4812
 
9.7%
t3451
 
7.0%
3319
 
6.7%
y3187
 
6.4%
r2711
 
5.5%
d2560
 
5.2%
N2302
 
4.7%
w2302
 
4.7%
i2292
 
4.6%
Other values (15)13702
27.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)49478
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e8840
17.9%
l4812
 
9.7%
t3451
 
7.0%
3319
 
6.7%
y3187
 
6.4%
r2711
 
5.5%
d2560
 
5.2%
N2302
 
4.7%
w2302
 
4.7%
i2292
 
4.6%
Other values (15)13702
27.7%

super_built_up_area
Real number (ℝ)

High correlation  Missing 

Distinct593
Distinct (%)31.0%
Missing1888
Missing (%)49.6%
Infinite0
Infinite (%)0.0%
Mean1921.6583
Minimum89
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2025-11-26T19:47:22.472770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile761.9
Q11457
median1828
Q32215
95-th percentile3187.1
Maximum10000
Range9911
Interquartile range (IQR)758

Descriptive statistics

Standard deviation767.16017
Coefficient of variation (CV)0.39921779
Kurtosis10.083066
Mean1921.6583
Median Absolute Deviation (MAD)372
Skewness1.8232285
Sum3679975.6
Variance588534.73
MonotonicityNot monotonic
2025-11-26T19:47:22.873070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
195038
 
1.0%
165038
 
1.0%
200026
 
0.7%
157825
 
0.7%
215023
 
0.6%
164022
 
0.6%
240820
 
0.5%
135019
 
0.5%
190019
 
0.5%
193018
 
0.5%
Other values (583)1667
43.8%
(Missing)1888
49.6%
ValueCountFrequency (%)
891
< 0.1%
1451
< 0.1%
1611
< 0.1%
2151
< 0.1%
2161
< 0.1%
3251
< 0.1%
3401
< 0.1%
3521
< 0.1%
3801
< 0.1%
4061
< 0.1%
ValueCountFrequency (%)
100001
< 0.1%
69261
< 0.1%
60001
< 0.1%
58002
0.1%
55141
< 0.1%
53502
0.1%
52002
0.1%
48901
< 0.1%
48572
0.1%
48482
0.1%

built_up_area
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct644
Distinct (%)37.2%
Missing2070
Missing (%)54.4%
Infinite0
Infinite (%)0.0%
Mean2360.2414
Minimum2
Maximum737147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2025-11-26T19:47:23.232455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile246.4
Q11100
median1650
Q32399
95-th percentile4662
Maximum737147
Range737145
Interquartile range (IQR)1299

Descriptive statistics

Standard deviation17719.603
Coefficient of variation (CV)7.5075385
Kurtosis1710.1077
Mean2360.2414
Median Absolute Deviation (MAD)642
Skewness41.21758
Sum4090298.4
Variance3.1398434 × 108
MonotonicityNot monotonic
2025-11-26T19:47:23.684296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180041
 
1.1%
324037
 
1.0%
190034
 
0.9%
135034
 
0.9%
270033
 
0.9%
90028
 
0.7%
160026
 
0.7%
130025
 
0.7%
200025
 
0.7%
170023
 
0.6%
Other values (634)1427
37.5%
(Missing)2070
54.4%
ValueCountFrequency (%)
21
 
< 0.1%
141
 
< 0.1%
301
 
< 0.1%
331
 
< 0.1%
503
0.1%
531
 
< 0.1%
551
 
< 0.1%
561
 
< 0.1%
571
 
< 0.1%
605
0.1%
ValueCountFrequency (%)
7371471
 
< 0.1%
135001
 
< 0.1%
112861
 
< 0.1%
95001
 
< 0.1%
90007
0.2%
87751
 
< 0.1%
82861
 
< 0.1%
8067.81
 
< 0.1%
80001
 
< 0.1%
75002
 
0.1%

carpet_area
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct733
Distinct (%)37.7%
Missing1859
Missing (%)48.9%
Infinite0
Infinite (%)0.0%
Mean2483.4669
Minimum15
Maximum607936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2025-11-26T19:47:24.109043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile348.3
Q1824
median1294
Q31786.25
95-th percentile2945.8
Maximum607936
Range607921
Interquartile range (IQR)962.25

Descriptive statistics

Standard deviation22375.239
Coefficient of variation (CV)9.0096787
Kurtosis627.83936
Mean2483.4669
Median Absolute Deviation (MAD)472
Skewness24.796084
Sum4827859.7
Variance5.0065133 × 108
MonotonicityNot monotonic
2025-11-26T19:47:24.461008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140042
 
1.1%
180036
 
0.9%
160036
 
0.9%
120032
 
0.8%
150030
 
0.8%
165028
 
0.7%
135028
 
0.7%
130023
 
0.6%
145023
 
0.6%
100022
 
0.6%
Other values (723)1644
43.2%
(Missing)1859
48.9%
ValueCountFrequency (%)
151
 
< 0.1%
331
 
< 0.1%
481
 
< 0.1%
501
 
< 0.1%
591
 
< 0.1%
601
 
< 0.1%
661
 
< 0.1%
721
 
< 0.1%
76.443
0.1%
77.312
0.1%
ValueCountFrequency (%)
6079361
< 0.1%
5692431
< 0.1%
5143961
< 0.1%
645291
< 0.1%
644121
< 0.1%
581411
< 0.1%
549171
< 0.1%
488111
< 0.1%
459661
< 0.1%
344011
< 0.1%

study room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.8 KiB
0
3082 
1
721 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3803
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
03082
81.0%
1721
 
19.0%

Length

2025-11-26T19:47:24.798675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T19:47:25.042513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
03082
81.0%
1721
 
19.0%

Most occurring characters

ValueCountFrequency (%)
03082
81.0%
1721
 
19.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
03082
81.0%
1721
 
19.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
03082
81.0%
1721
 
19.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
03082
81.0%
1721
 
19.0%

servant room
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.8 KiB
0
2446 
1
1357 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3803
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
02446
64.3%
11357
35.7%

Length

2025-11-26T19:47:25.332183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T19:47:25.533466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02446
64.3%
11357
35.7%

Most occurring characters

ValueCountFrequency (%)
02446
64.3%
11357
35.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02446
64.3%
11357
35.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02446
64.3%
11357
35.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02446
64.3%
11357
35.7%

store room
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.8 KiB
0
3459 
1
 
344

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3803
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
03459
91.0%
1344
 
9.0%

Length

2025-11-26T19:47:26.158126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T19:47:26.368116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
03459
91.0%
1344
 
9.0%

Most occurring characters

ValueCountFrequency (%)
03459
91.0%
1344
 
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
03459
91.0%
1344
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
03459
91.0%
1344
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
03459
91.0%
1344
 
9.0%

pooja room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.8 KiB
0
3140 
1
663 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3803
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
03140
82.6%
1663
 
17.4%

Length

2025-11-26T19:47:26.666113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T19:47:26.839627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
03140
82.6%
1663
 
17.4%

Most occurring characters

ValueCountFrequency (%)
03140
82.6%
1663
 
17.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
03140
82.6%
1663
 
17.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
03140
82.6%
1663
 
17.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
03140
82.6%
1663
 
17.4%

others
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.8 KiB
0
3382 
1
421 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3803
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03382
88.9%
1421
 
11.1%

Length

2025-11-26T19:47:27.086906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T19:47:27.297156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
03382
88.9%
1421
 
11.1%

Most occurring characters

ValueCountFrequency (%)
03382
88.9%
1421
 
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
03382
88.9%
1421
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
03382
88.9%
1421
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
03382
88.9%
1421
 
11.1%

furnishing_type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.8 KiB
0
2501 
2
1081 
1
 
221

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row0
5th row2

Common Values

ValueCountFrequency (%)
02501
65.8%
21081
28.4%
1221
 
5.8%

Length

2025-11-26T19:47:27.615356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T19:47:27.858783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02501
65.8%
21081
28.4%
1221
 
5.8%

Most occurring characters

ValueCountFrequency (%)
02501
65.8%
21081
28.4%
1221
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02501
65.8%
21081
28.4%
1221
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02501
65.8%
21081
28.4%
1221
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02501
65.8%
21081
28.4%
1221
 
5.8%

luxury_score
Real number (ℝ)

Zeros 

Distinct161
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.947936
Minimum0
Maximum174
Zeros486
Zeros (%)12.8%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2025-11-26T19:47:28.145855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median58
Q3109
95-th percentile174
Maximum174
Range174
Interquartile range (IQR)78

Descriptive statistics

Standard deviation52.821789
Coefficient of variation (CV)0.74451481
Kurtosis-0.85533655
Mean70.947936
Median Absolute Deviation (MAD)37
Skewness0.47028839
Sum269815
Variance2790.1414
MonotonicityNot monotonic
2025-11-26T19:47:28.500279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0486
 
12.8%
49353
 
9.3%
174196
 
5.2%
4462
 
1.6%
3858
 
1.5%
7256
 
1.5%
16555
 
1.4%
6050
 
1.3%
3749
 
1.3%
4246
 
1.2%
Other values (151)2392
62.9%
ValueCountFrequency (%)
0486
12.8%
56
 
0.2%
66
 
0.2%
743
 
1.1%
830
 
0.8%
99
 
0.2%
127
 
0.2%
1310
 
0.3%
1412
 
0.3%
1543
 
1.1%
ValueCountFrequency (%)
174196
5.2%
1691
 
< 0.1%
1689
 
0.2%
16721
 
0.6%
16611
 
0.3%
16555
 
1.4%
1613
 
0.1%
16028
 
0.7%
15923
 
0.6%
15834
 
0.9%

Interactions

2025-11-26T19:47:02.941308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:34.057645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:37.265950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:40.298298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:43.149045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:46.178194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:49.335996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:53.792057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:57.035079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:59.993151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:47:03.278677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:34.412997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:37.596025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:40.544384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:43.430479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:46.509986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:49.589824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:54.091337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:57.334416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:47:00.278323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:47:03.561168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:34.745401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:37.921879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:40.808726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:43.746814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:46.840891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:49.886028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:54.473888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:57.633049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:47:00.652264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:47:03.818977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:35.121443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:38.185289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:41.042130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:44.058685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:47.117557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:50.276656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:54.825130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:57.916777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:47:00.967776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:47:04.128869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:35.468128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:38.475266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:41.377113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:44.341716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:47.429350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:51.923681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:55.147441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:58.274918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:47:01.240177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:47:04.507205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:35.799445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:38.773480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:41.712314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:44.632219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:47.785438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:52.212319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:55.503340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:58.587742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:47:01.514942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:47:04.781048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:36.092607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:39.102462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:41.983745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:44.937437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:48.093287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:52.468585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:55.814351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:58.866705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:47:01.800626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:47:05.064474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:36.396622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:39.391729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:42.254332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:45.287450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:48.378838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:52.771001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:56.101735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:59.125655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:47:02.117196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:47:05.360114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:36.714009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:39.671007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:42.539771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:45.619205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:48.669903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:53.135451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:56.401202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:59.471816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:47:02.351458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:47:05.678905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:36.993091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:39.964869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:42.872668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:45.895407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:48.985189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:53.457916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:56.712418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:46:59.697415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T19:47:02.645757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-26T19:47:28.829019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
priceprice_per_sqftareabedRoombathroomfloorNumsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
price1.0000.3970.0200.5250.591-0.0910.7710.024-0.0100.2560.4000.3060.341-0.0060.2370.097
price_per_sqft0.3971.000-0.0270.3130.294-0.1100.278-0.021-0.0360.0940.1010.0860.1210.0080.093-0.044
area0.020-0.0271.0000.0070.008-0.0150.9690.9650.9930.0180.0020.0030.0040.0230.001-0.007
bedRoom0.5250.3130.0071.0000.908-0.1300.736-0.000-0.0180.1380.2820.1830.2740.0700.186-0.015
bathroom0.5910.2940.0080.9081.000-0.0690.7440.006-0.0150.1620.4150.1970.2860.0630.2360.100
floorNum-0.091-0.110-0.015-0.130-0.0691.0000.1570.010-0.028-0.0560.095-0.089-0.062-0.0070.0310.173
super_built_up_area0.7710.2780.9690.7360.7440.1571.0000.9550.9240.0310.5580.0500.142-0.0070.1560.211
built_up_area0.024-0.0210.965-0.0000.0060.0100.9551.0000.9720.0030.0090.0080.006-0.0100.0180.006
carpet_area-0.010-0.0360.993-0.018-0.015-0.0280.9240.9721.0000.025-0.017-0.009-0.0120.032-0.028-0.012
study room0.2560.0940.0180.1380.162-0.0560.0310.0030.0251.0000.1830.2240.3150.0370.1280.151
servant room0.4000.1010.0020.2820.4150.0950.5580.009-0.0170.1831.0000.1630.252-0.0090.2600.335
store room0.3060.0860.0030.1830.197-0.0890.0500.008-0.0090.2240.1631.0000.307-0.1050.1620.186
pooja room0.3410.1210.0040.2740.286-0.0620.1420.006-0.0120.3150.2520.3071.0000.0390.2090.178
others-0.0060.0080.0230.0700.063-0.007-0.007-0.0100.0320.037-0.009-0.1050.0391.0000.039-0.059
furnishing_type0.2370.0930.0010.1860.2360.0310.1560.018-0.0280.1280.2600.1620.2090.0391.0000.328
luxury_score0.097-0.044-0.007-0.0150.1000.1730.2110.006-0.0120.1510.3350.1860.178-0.0590.3281.000
2025-11-26T19:47:29.431409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
priceprice_per_sqftareabedRoombathroomfloorNumsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
price1.0000.7430.7450.6830.7210.0030.7740.6040.6220.1960.5100.2410.268-0.0050.2980.217
price_per_sqft0.7431.0000.2060.4110.406-0.1200.2860.1290.1430.1580.2460.2090.198-0.0270.2290.057
area0.7450.2061.0000.6310.6920.1150.9490.8370.8060.1540.5610.1680.2240.0250.2160.259
bedRoom0.6830.4110.6311.0000.863-0.1000.8020.3850.5770.1160.4300.2010.2970.0360.2120.062
bathroom0.7210.4060.6920.8631.000-0.0060.8220.4680.6070.1420.5640.1930.2830.0370.2560.181
floorNum0.003-0.1200.115-0.100-0.0061.0000.1550.0880.151-0.0480.120-0.083-0.0660.0020.0460.223
super_built_up_area0.7740.2860.9490.8020.8220.1551.0000.9270.895-0.0180.6600.0340.110-0.0190.1650.227
built_up_area0.6040.1290.8370.3850.4680.0880.9271.0000.9680.1840.4180.1820.237-0.0080.1910.290
carpet_area0.6220.1430.8060.5770.6070.1510.8950.9681.0000.0670.4780.0440.1130.0120.1880.236
study room0.1960.1580.1540.1160.142-0.048-0.0180.1840.0671.0000.1830.2240.3150.0370.1340.159
servant room0.5100.2460.5610.4300.5640.1200.6600.4180.4780.1831.0000.1630.252-0.0090.2670.331
store room0.2410.2090.1680.2010.193-0.0830.0340.1820.0440.2240.1631.0000.307-0.1050.1640.186
pooja room0.2680.1980.2240.2970.283-0.0660.1100.2370.1130.3150.2520.3071.0000.0390.2140.175
others-0.005-0.0270.0250.0360.0370.002-0.019-0.0080.0120.037-0.009-0.1050.0391.0000.043-0.054
furnishing_type0.2980.2290.2160.2120.2560.0460.1650.1910.1880.1340.2670.1640.2140.0431.0000.321
luxury_score0.2170.0570.2590.0620.1810.2230.2270.2900.2360.1590.3310.1860.175-0.0540.3211.000
2025-11-26T19:47:30.028787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
property_typepriceprice_per_sqftareabedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
property_type1.0000.6940.2650.0400.7910.6050.1720.6140.1220.480NaN0.0000.0000.2000.1090.3710.3880.0370.0490.414
price0.6941.0000.4500.1750.6160.6510.3160.2480.0460.1880.7530.0000.0000.3160.4790.3910.4350.0430.2800.174
price_per_sqft0.2650.4501.0000.0000.2570.2400.0540.0000.0000.085NaN0.0000.0000.0390.0550.0000.0590.0470.0330.108
area0.0400.1750.0001.0000.0000.0760.0160.0000.0490.000NaN1.0000.9820.0260.0210.0540.0520.0580.1000.000
bedRoom0.7910.6160.2570.0001.0000.9710.5030.2900.1000.2680.6000.0000.0000.2200.5770.2820.3980.0970.3180.267
bathroom0.6050.6510.2400.0760.9711.0000.5480.2140.0950.2320.7500.0000.0000.2340.6720.2810.4020.0920.3450.253
balcony0.1720.3160.0540.0160.5030.5481.0000.1870.0230.3260.4600.0000.0310.1490.3610.1170.1590.0660.2330.487
floorNum0.6140.2480.0000.0000.2900.2140.1871.0000.0000.2280.1570.0000.0000.1020.1040.1420.1310.0370.0440.253
facing0.1220.0460.0000.0490.1000.0950.0230.0001.0000.1660.000NaN0.0000.0000.0570.0440.0370.0000.0800.133
agePossession0.4800.1880.0850.0000.2680.2320.3260.2280.1661.0000.1450.0000.0000.1580.4010.2000.2600.1430.4700.403
super_built_up_areaNaN0.753NaNNaN0.6000.7500.4600.1570.0000.1451.000NaNNaN0.1550.7640.0580.2050.1100.2070.209
built_up_area0.0000.0000.0001.0000.0000.0000.0000.000NaN0.000NaN1.000NaN0.0000.0000.0000.0000.0000.0510.000
carpet_area0.0000.0000.0000.9820.0000.0000.0310.0000.0000.000NaNNaN1.0000.0060.0000.0000.0000.0250.0000.000
study room0.2000.3160.0390.0260.2200.2340.1490.1020.0000.1580.1550.0000.0061.0000.2820.3420.4730.0500.0860.242
servant room0.1090.4790.0550.0210.5770.6720.3610.1040.0570.4010.7640.0000.0000.2821.0000.2510.3840.0000.1670.452
store room0.3710.3910.0000.0540.2820.2810.1170.1420.0440.2000.0580.0000.0000.3420.2511.0000.4610.1610.0980.297
pooja room0.3880.4350.0590.0520.3980.4020.1590.1310.0370.2600.2050.0000.0000.4730.3840.4611.0000.0540.1320.249
others0.0370.0430.0470.0580.0970.0920.0660.0370.0000.1430.1100.0000.0250.0500.0000.1610.0541.0000.0330.226
furnishing_type0.0490.2800.0330.1000.3180.3450.2330.0440.0800.4700.2070.0510.0000.0860.1670.0980.1320.0331.0000.377
luxury_score0.4140.1740.1080.0000.2670.2530.4870.2530.1330.4030.2090.0000.0000.2420.4520.2970.2490.2260.3771.000

Missing values

2025-11-26T19:47:06.397284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-26T19:47:07.011516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-26T19:47:07.872592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
0flatdlf the skycourtsector 861.588190.01929.0Super Built up area 1929(179.21 sq.m.)Built Up area: 1750 sq.ft. (162.58 sq.m.)Carpet area: 1450 sq.ft. (134.71 sq.m.)33116.0NorthRelatively New1929.01750.01450.0000102152
1flatss the leafsector 851.207317.01640.0Super Built up area 1640(152.36 sq.m.)22312.0SouthRelatively New1640.0NaNNaN000002157
2houseansals florence villasector 576.0022222.02700.0Plot area 300(250.84 sq.m.)4522.0NaNOld PropertyNaN2700.0NaN01010220
3flatrof anandasector 950.2161.034426.0Carpet area: 34401 (3195.96 sq.m.)11113.0NorthRelatively NewNaNNaN34401.000000068
4houseemaar mgf marbellasector 66NaNNaNNaNPlot area 500(418.06 sq.m.)573+2.0NaNModerately OldNaN500.0NaN11110270
5flatvatika the seven lampssector 820.866022.01428.0Super Built up area 1430(132.85 sq.m.)22212.0EastRelatively New1430.0NaNNaN100002135
6houseindependentsector 70.455000.0900.0Plot area 900(83.61 sq.m.)3212.0North-WestOld PropertyNaN900.0NaN00001012
7flatshree vardhman florasector 900.705177.01352.0Carpet area: 1352 (125.6 sq.m.)2327.0NorthRelatively NewNaNNaN1352.010000249
8flatraheja atharvasector 1091.605319.03008.0Built Up area: 3008 (279.45 sq.m.)4100.0NaNNew PropertyNaN3008.0NaN0000000
9flatsuncity vatsal valleygwal pahari1.3812212.01130.0Built Up area: 1130 (104.98 sq.m.)2224.0EastUnder ConstructionNaN1130.0NaN000002133
property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
3793flatbrisk lumbini terrace homessector 1091.386338.02177.0Carpet area: 2177 (202.25 sq.m.)3314.0NaNModerately OldNaNNaN2177.000000020
3794flatgodrej summitsector 1040.855878.01446.0Super Built up area 1446(134.34 sq.m.)Built Up area: 1269 sq.ft. (117.89 sq.m.)Carpet area: 777 sq.ft. (72.19 sq.m.)223+10.0EastRelatively New1446.01269.0777.010000079
3795flatsignature global the millennia 3sector 37d0.457627.0590.0Carpet area: 590 (54.81 sq.m.)2328.0NaNUndefinedNaNNaN590.00000008
3796flatdlf park placesector 544.8525866.01875.0Super Built up area 1875(174.19 sq.m.)343+25.0South-EastRelatively New1875.0NaNNaN01000226
3797flatexperion the heartsongsector 1082.007196.02779.0Super Built up area 2779(258.18 sq.m.)Built Up area: 2500 sq.ft. (232.26 sq.m.)Carpet area: 2300 sq.ft. (213.68 sq.m.)443+10.0North-EastRelatively New2779.02500.02300.0010102174
3798flatdlf the primussector 82a2.2510714.02100.0Carpet area: 2100 (195.1 sq.m.)333+29.0EastRelatively NewNaNNaN2100.001000172
3799housebestech park view aindependentdasector 814.357937.05481.0Plot area 5480(509.11 sq.m.)4432.0NorthRelatively NewNaN5480.0NaN01100051
3800flatshree arihant apartmentsector 541.558611.01800.0Super Built up area 2000(185.81 sq.m.)Built Up area: 1900 sq.ft. (176.52 sq.m.)Carpet area: 1800 sq.ft. (167.23 sq.m.)3333.0North-EastModerately Old2000.01900.01800.0010002109
3801flataipl zen residencessector 70a1.3410276.01304.0Super Built up area 1304(121.15 sq.m.)223+17.0EastNew Property1304.0NaNNaN000002110
3802flatcentral park 2 bellevuesector 485.0011627.04300.0Carpet area: 4300 (399.48 sq.m.)34314.0North-EastModerately OldNaNNaN4300.000000082

Duplicate rows

Most frequently occurring

property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score# duplicates
0flatambience caitrionasector 2414.00200000.0700.0Built Up area: 700 (65.03 sq.m.)4533.0EastUndefinedNaN700.0NaN00000002
1flatansal heights 86sector 860.905325.01690.0Built Up area: 1690 (157.01 sq.m.)33210.0NaNNew PropertyNaN1690.0NaN000000292
2flatansal heights 86sector 861.304666.02786.0Super Built up area 2786(258.83 sq.m.)46211.0EastNew Property2786.0NaNNaN010010862
3flatansal housing highland parksector 1030.886429.01369.0Super Built up area 1361(126.44 sq.m.)2233.0NaNNew Property1361.0NaNNaN000000522
4flatantriksh heightssector 840.855556.01530.0Super Built up area 1350(125.42 sq.m.)22310.0North-WestNew Property1350.0NaNNaN100010242
5flatapartmentsector 920.754687.01600.0Carpet area: 1600 (148.64 sq.m.)3432.0EastModerately OldNaNNaN1600.01000001132
6flatashiana anmolsohna road0.8811125.0791.0Super Built up area 1275(118.45 sq.m.)Carpet area: 791 sq.ft. (73.49 sq.m.)22213.0EastRelatively New1275.0NaN791.00000011272
7flatassotech blithsector 990.926739.01365.0Super Built up area 1365(126.81 sq.m.)223+22.0NaNUnder Construction1365.0NaNNaN000000562
8flatassotech blithsector 991.906702.02835.0Built Up area: 2835 (263.38 sq.m.)443+2.0North-EastUndefinedNaN2835.0NaN000000512
9flatats tourmalinesector 1092.308897.02585.0Super Built up area 2585(240.15 sq.m.)343+10.0EastNew Property2585.0NaNNaN010010742